Scaling up: Spark and Big Data

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Related lectures (31)

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Big Data Challenges: Scaling to Massive Data

Explores challenges of handling massive data in the era of big data, discussing solutions like MapReduce and Spark.

Big Data Challenges: Distributed Computing with Spark

Explores big data challenges, distributed computing with Spark, RDDs, hardware requirements, MapReduce, transformations, and Spark DataFrames.

Big Data Best Practices and Guidelines

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Advanced Spark Optimization Techniques: Managing Big Data

Discusses advanced Spark optimization techniques for managing big data efficiently, focusing on parallelization, shuffle operations, and memory management.

Logistic Regression: Vegetation Prediction

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Data Wrangling with Hive: Managing Big Data Efficiently

Covers data wrangling techniques using Apache Hive for efficient big data management.

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Hadoop Ecosystem: Architectural Choices & MapReduce Programming

Explores the Hadoop ecosystem's architecture and MapReduce programming model, emphasizing strengths and limitations.

General Introduction to Big Data

Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.